Title:
Co-evolution of shaping rewards and meta-parameters in reinforcement learning
Co-evolution of shaping rewards and meta-parameters in reinforcement learning
| dc.contributor.author | Elfwing, Stefan | |
| dc.contributor.author | Uchibe, Eiji | |
| dc.contributor.author | Doya, Kenji | |
| dc.contributor.author | Christensen, Henrik I. | |
| dc.contributor.corporatename | Georgia Institute of Technology. College of Computing | |
| dc.contributor.corporatename | Okinawa Institute of Science and Technology. Neural Computation Unit | |
| dc.contributor.corporatename | Georgia Institute of Technology. Center for Robotics and Intelligent Machines | |
| dc.contributor.corporatename | Kungl. Tekniska Högskolan. Centrum för Autonoma System | |
| dc.date.accessioned | 2011-03-22T19:44:24Z | |
| dc.date.available | 2011-03-22T19:44:24Z | |
| dc.date.issued | 2008-12 | |
| dc.description | Digital Object Identifier: 10.1177/1059712308092835 | en_US |
| dc.description.abstract | In this article, we explore an evolutionary approach to the optimization of potential-based shaping rewards and meta-parameters in reinforcement learning. Shaping rewards is a frequently used approach to increase the learning performance of reinforcement learning, with regards to both initial performance and convergence speed. Shaping rewards provide additional knowledge to the agent in the form of richer reward signals, which guide learning to high-rewarding states. Reinforcement learning depends critically on a few meta-parameters that modulate the learning updates or the exploration of the environment, such as the learning rate α, the discount factor of future rewards γ, and the temperature τ that controls the trade-off between exploration and exploitation in softmax action selection. We validate the proposed approach in simulation using the mountain-car task. We also transfer shaping rewards and meta-parameters, evolutionarily obtained in simulation, to hardware, using a robotic foraging task. | en_US |
| dc.identifier.citation | Elfwing, S., Uchibe, E., Doya, K., and Christensen, H. I. Co-evolution of shaping rewards and meta-parameters in reinforcement learning. Adaptive Behaviour 16, 8 (Dec 2008), 400-412. | en_US |
| dc.identifier.doi | 10.1177/1059712308092835 | |
| dc.identifier.issn | 1059-7123 | |
| dc.identifier.uri | http://hdl.handle.net/1853/38251 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | Georgia Institute of Technology | en_US |
| dc.publisher.original | Sage | |
| dc.publisher.original | International Society for Adaptive Behavior | |
| dc.subject | Shaping rewards | en_US |
| dc.subject | Reinforcement learning | en_US |
| dc.title | Co-evolution of shaping rewards and meta-parameters in reinforcement learning | en_US |
| dc.type | Text | |
| dc.type.genre | Article | |
| dspace.entity.type | Publication | |
| local.contributor.author | Christensen, Henrik I. | |
| local.contributor.corporatename | Institute for Robotics and Intelligent Machines (IRIM) | |
| relation.isAuthorOfPublication | afdc727f-2705-4744-945f-e7d414f2212b | |
| relation.isOrgUnitOfPublication | 66259949-abfd-45c2-9dcc-5a6f2c013bcf |